Methods and systems for dynamic price negotiation

Abstract

A computer-implemented method for negotiating a price of a product for a user may comprise obtaining an identification of the user via a device associated with the user; obtaining social influence data of the user based on the identification of the user, wherein the social influence data of the user includes a net promoter score or a social ranking of the user; obtaining purchase parameter data of the user based on the identification of the user, wherein the purchase parameter data of the user includes a credit score, an income range, or a transaction history of the user; determining a user-specific price of the product based on the purchase parameter data and the social influence data using a trained machine learning model; and transmitting, to the user, a notification indicative of the user-specific price.

What is claimed is: 
     
       1. A computer-implemented method for negotiating a price of a product for a user, the method comprising:
 obtaining, by one or more processors, an identification of the user from a user device associated with the user, wherein:
 the identification of the user comprises one or more of an actual name, a social security number, or a phone number associated with the user; and 
 the user device is one of:
 a near-field communication (NFC) card, wherein the identification of the user is obtained from a scan of a component of the NFC card by an electronic reader; or 
 an electronic mobile device, wherein the identification of the user is obtained from a scan of a graphical component by a camera of the electronic mobile device; 
 
 
 determining, by the one or more processors, whether the user is authenticated by comparing the identification of the user with a prestored identification; 
 upon determining that the user is authenticated, obtaining, by the one or more processors, raw data from one or more social networks associated with the user, wherein the raw data comprises one or more of:
 retweets; 
 list or group memberships; 
 quantity of spam or dead accounts following the user; or 
 degree of influence of people who retweet user; 
 
 generating, by the one or more processors, social influence data associated with the user based on the raw data, wherein the social influence data comprises one or more of:
 a net promoter score; 
 a social ranking; 
 a social reach; 
 an amplification score; or 
 a network impact; 
 
 based on the identification of the user, obtaining, by the one or more processors, purchase parameter data associated with the user, wherein the purchase parameter data comprises one or more of:
 a credit score associated with the user; or 
 an income range of the user; 
 
 determining, by the one or more processors, using a trained machine learning model, a user-specific price of a product based on the purchase parameter data of the user and the social influence data of the user, wherein:
 the trained machine learning model is trained, using supervised, un-supervised, or semi-supervised learning, based on (i) training user data that includes information regarding purchase parameter data and social influence data associated with persons other than the user; and (ii) training price data that includes user-specific prices for one or more products associated with the persons other than the user, to learn relationships between the training user data and the training price data, such that the trained machine learning model is configured to determine a user-specific price of a product for a user upon input of the purchase parameter data of the user and the social influence data of the user; and 
 the trained machine learning model is configured to utilize principal component analysis; and 
 the determined user-specific price of a product is stored for a period of time and expires after the period of time, and during the period of time, the determined user-specific price of the product is available for further analysis; and 
 
 transmitting, to the user, a notification indicative of the user-specific price. 
 
     
     
       2. The method of  claim 1 , wherein the user-specific price of the product includes additional offers including at least one of a customer loyalty reward, an incentive to purchase the product again, or an incentive to promote the product. 
     
     
       3. The method of  claim 1 , further including storing the user-specific price of the product for subsequent analysis. 
     
     
       4. The method of  claim 1 , wherein the notification is configured to be displayed on a display screen of the electronic mobile device. 
     
     
       5. The method of  claim 1 , wherein the obtaining the purchase parameter data associated with the user includes obtaining the purchase parameter data of the user from a transactional entity over a network. 
     
     
       6. The method of  claim 5 , wherein the transactional entity is a financial services providers. 
     
     
       7. The method of  claim 1 , wherein the user device is the NFC card, and wherein the electronic reader is associated with a product. 
     
     
       8. The method of  claim 1 , wherein the user device is the NFC card and includes a radio-frequency identification (RFID) chip. 
     
     
       9. The method of  claim 1 , wherein the purchase parameter data is obtained from a scan of a component of the NFC card by an electronic reader after determining that the user is authenticated. 
     
     
       10. A computer-implemented method for negotiating a price of a product for a user, the method comprising:
 obtaining, by one or more processors, an identification of the user from a user device associated with the user, wherein:
 the identification of the user comprises one or more of an actual name, a social security number, or a phone number associated with the user; and 
 the user device is one of:
 an NFC card, wherein the identification of the user is obtained from a scan of a component of the NFC card by an electronic reader; or 
 an electronic mobile device, wherein the identification of the user is obtained from a scan of a graphical component by a camera of the electronic mobile device; 
 
 
 determining, by the one or more processors, whether the user is authenticated by comparing the identification of the user with a prestored identification; 
 upon determining that the user is authenticated, obtaining, by the one or more processors, raw data from one or more social networks associated with the user, wherein the raw data comprises one or more of:
 retweets; 
 list or group memberships; 
 quantity of spam or dead accounts following the user; or 
 degree of influence of people who retweet user; 
 
 generating, by the one or more processors, social influence data associated with the user based on the raw data, wherein the social influence data comprises one or more of:
 a net promoter score; 
 a social ranking; 
 a social reach; 
 an amplification score; or 
 a network impact; 
 
 based on the identification of the user, obtaining, by the one or more processors, purchase parameter data associated with the user, wherein the purchase parameter data comprises:
 a credit score associated with the user; and 
 an income range of the user; 
 
 determining, by a trained machine learning model, a user-specific price of the product based on the purchase parameter data of the user and the social influence data of the user, wherein:
 the trained machine learning model has been trained using supervised, un-supervised, or semi-supervised learning, to determine one or more user-specific prices for one or more products, based on (i) training user data that includes information regarding purchase parameter data and social influence data associated with persons other than the user; and (ii) training price data that includes user-specific prices for one or more products associated with the persons other than the user, to learn relationships between the training user data and the training price data, such that the machine learning model is configured to determine a user-specific price of a product for a user upon input of the purchase parameter data of the user and the social influence data of the user; and 
 the determined user-specific price of a product is stored for a period of time and expires after the period of time; 
 
 transmitting, via the one or more processors, a notification to the user indicative of the user-specific price; and 
 receiving, via the one or more processors, a user request to negotiate the user-specific price. 
 
     
     
       11. The method of  claim 10 , wherein the user-specific price of the product includes additional offers including at least one of a customer loyalty reward, an incentive to purchase the product again, or an incentive to promote the product. 
     
     
       12. The method of  claim 10 , further including storing the user-specific price of the product for subsequent analysis. 
     
     
       13. The method of  claim 10 , wherein the user-specific price of the product is less than an original price of the product. 
     
     
       14. The method of  claim 10 , wherein the notification is configured to be displayed on a display screen of the electronic mobile device associated with the user. 
     
     
       15. The method of  claim 10 , wherein the user device is the NFC card, and wherein the electronic reader is associated with a product. 
     
     
       16. The method of  claim 10 , wherein the user device is the NFC card and includes a radio-frequency identification (RFID) chip. 
     
     
       17. The method of  claim 10 , wherein the purchase parameter data is obtained from a scan of a component of the NFC card by an electronic reader after determining that the user is authenticated. 
     
     
       18. The method of  claim 10 , wherein the obtaining the purchase parameter data associated with the user includes obtaining the purchase parameter data of the user from a transactional entity over a network. 
     
     
       19. The method of  claim 18 , wherein the transactional entity is a financial services providers. 
     
     
       20. A computer system for negotiating a price of a product for a user, comprising:
 a memory storing instructions; and 
 one or more processors configured to execute the instructions to perform operations including:
 obtaining an identification of the user from a user device associated with the user, wherein:
 the identification of the user comprises one or more of a social security number or a phone number associated with the user; and 
 the user device is one of:
 a near-field communication (NFC) card, wherein the identification of the user is obtained from a scan of a component of the NFC card by an electronic reader; or 
 an electronic mobile device, wherein the identification of the user is obtained from a scan of a graphical component by a camera of the electronic mobile device; 
 
 
 determining whether the user is authenticated by comparing the identification of the user with a prestored identification; 
 
 upon determining that the user is authenticated, obtaining raw data from one or more social networks associated with the user, wherein the raw data comprises one or more of:
 retweets; 
 list or group memberships; 
 quantity of spam or dead accounts following the user; or 
 degree of influence of people who retweet user; 
 
 generating, by the one or more processors, social influence data associated with the user based on the raw data, wherein the social influence data comprises one or more of:
 a net promoter score; 
 a social ranking; 
 a social reach; 
 an amplification score; or 
 a network impact; 
 based on the identification of the user, obtaining, from a financial services providers, purchase parameter data associated with the user, wherein the purchase parameter data comprises one or more of: 
 a credit score associated with the user; or 
 an income range of the user; 
 determining, using a trained machine learning model, a user-specific price of the product based on the purchase parameter data of the user and the social influence data of the user, wherein:
 the trained machine learning model is trained, using supervised, un-supervised, or semi-supervised learning, based on (i) training user data that includes information regarding purchase parameter data and social influence data associated with persons other than the user; and (ii) training price data that includes user-specific prices for one or more products associated with the persons other than the user, to learn relationships between the training user data and the training price data, such that the trained machine learning model is configured to determine a user-specific price of a product for a user upon input of the purchase parameter data of the user and the social influence data of the user; and 
 the trained machine learning model is configured to utilize principal component analysis; 
 the determined user-specific price of a product is stored for a period of time and expires after the period of time; and 
 
 transmitting, to the user, a notification indicative of the user-specific price.